Introduction to Pattern Recognition [IntroPR]

Summary

The goal of this lecture is to familiarize the students with the overall
pipeline of a Pattern Recognition System. The various steps involved from
data capture to pattern classification are presented. The lectures start
with a short introduction, where the nomenclature is defined. Analog to
digital conversion is briefly discussed with a focus on how it impacts
further signal analysis. Commonly used preprocessing methods are then
described. A key component of Pattern Recognition is feature extraction.
Thus, several techniques for feature computation will be presented including
Walsh Transform, Haar Transform, Linear Predictive Coding, Wavelets,
Moments, Principal Component Analysis and Linear Discriminant Analysis. The
lectures conclude with a basic introduction to classification. The
principles of statistical, distribution-free and nonparametric
classification approaches will be presented. Within this context we will
cover Bayesian and Gaussian classifiers, as well as artificial neural
networks. The accompanying exercises will provide further details on the
methods and procedures presented in this lecture with particular emphasis on
their application.

General Information

The lecture video is available here (only accessible if you are inside the university network; if you want to watch the videos from home, consider to tunnel the connection).

A. Exam Dates

Monday 18.02.2013 (only in the morning)

Tuesday 19.02.2013

Wednesday 20.02.2013 (only in the morning)

Wednesday 20.03.2013

Thursday 21.03.2013

Tuesday 09.04.2013

Wednesday 10.04.2013

B. Signing up for the Exam

Reserving a slot for the exam is only possible afterJanuary 6th, 2013.

You must reserve a time-slot for the exam, independent of whether you have signed up at meinCampus. You can do so by:

either by personally visiting the secretaries at the Pattern Recognition Lab, at the 09.138 at Martenstr. 3, 91058 Erlangen,

or by sending them an email at Kristina Müller at mueller(at)cs.fau.de or at Iris Koppe at koppe(at)cs.fau.de . Make sure in your email to write your full name, student ID, program of Studies, birthdate, number of credits and type of exam (e.g. benoteter Schein, unbenoteter Schein, Prüfung durch meinCampus, etc.).

Slides

The updated slides will be posted on the web soon after the corresponding lecture is completed.

You may want to read the following paper which describes how Branch and Bound can be used in Feature Selection. Due to copyright issues, a copy can not be placed on this web-site. P.M. Narendra and K. Fukunaga, "A Branch and Bound Algorithm for Feature Subset Selection, " IEEE Transactions on Computers, Vol. C-26, No. 9, 1977, pp. 917-922.

A general overview on the Branch and Bound methodology is presented here.